Abstract
This article introduces the Zeffiro interface (ZI) version 2.2 for brain imaging. ZI aims to provide a simple, accessible and multimodal open source platform for finite element method (FEM) based and graphics processing unit (GPU) accelerated forward and inverse computations in the Matlab environment. It allows one to (1) generate a given multi-compartment head model, (2) to evaluate a lead field matrix as well as (3) to invert and analyze a given set of measurements. GPU acceleration is applied in each of the processing stages (1)–(3). In its current configuration, ZI includes forward solvers for electro-/magnetoencephalography (EEG) and linearized electrical impedance tomography (EIT) as well as a set of inverse solvers based on the hierarchical Bayesian model (HBM). We report the results of EEG and EIT inversion tests performed with real and synthetic data, respectively, and demonstrate numerically how the inversion parameters affect the EEG inversion outcome in HBM. The GPU acceleration was found to be essential in the generation of the FE mesh and the LF matrix in order to achieve a reasonable computing time. The code package can be extended in the future based on the directions given in this article.
Highlights
This article introduces the Zeffiro1 interface (ZI) version 2.2 for electromagnetic brain imaging and investigations
The finite element method (FEM) can be applied to model an electromagnetic source within the brain (Pursiainen et al 2016b; Miinalainen et al 2019) and, thereby, to construct a lead field (LF) matrix to localize brain activity in electro-/magnetoencephalography (EEG/MEG) (Hamalainen et al 1993; Niedermeyer and da Silva 2004)
The electromagnetic field within Ω can be evoked either by J p acting as the source, which is the case in EEG/MEG, or by an external source, e.g., a current pattern injected through contact electrodes in electrical impedance tomography (EIT)
Summary
This article introduces the Zeffiro interface (ZI) version 2.2 for electromagnetic brain imaging and investigations. The FEM is widely applied for modeling electromagnetic fields in a bounded domain, such as the brain and the head (de Munck et al 2012; Monk 2003). The same quasi-static set of Maxwell’s equations that predicts the electric potential field of a neural source can be applied to model the effect of current injections, where either direct or alternating currents applied through electrodes act as the source of the electromagnetic field. Such an approach is used, for example, in the electrical impedance tomography (EIT) (Cheney et al 1999) in which
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
